Qwen3-Omni-Flash-2025-12-01: Hear You. See You. Follow Smarter!

This article introduces the upgraded Qwen3-Omni-Flash-2025-12-01 model, which delivers smarter, more natural multimodal interaction across text, audio, images, and video.

Qwen3-Omni is a next-generation native multimodal large model capable of seamlessly processing multiple input modalities—including text, images, audio, and video—and generating both text and natural-sounding speech outputs simultaneously via real-time streaming responses. This version introduces multiple enhancements to improve model performance and efficiency.

Qwen3-Omni-Flash-2025-12-01 is a comprehensively upgraded iteration built upon Qwen3-Omni.

Key highlights of this upgraded version include:

Greatly Enhanced Audio-Visual Interaction Experience: Dramatically improved understanding and execution of audio-visual instructions, effectively resolving the “intelligence drop” issue commonly seen in casual spoken scenarios. Multi-turn audio-visual conversations now achieve significantly higher stability and coherence, enabling more natural and seamless interactions.
Strengthened System Prompt Control: Full customization of system prompts is now supported, enabling precise control over model behavior. Whether it’s persona style (e.g., sweet, cool, anime-inspired), colloquial tone preferences, or output length constraints—every detail can be finely tuned, offering unprecedented command over response characteristics.
More Reliable Multilingual Compliance: Supports text-based interaction in 119 languages, speech recognition in 19 languages, and speech synthesis in 10 languages. Language-following instability from the previous version has been fully addressed, ensuring accurate and consistent performance across diverse linguistic contexts.
More Human-Like and Fluent Speech Synthesis: Eliminates sluggish or robotic speech by significantly enhancing adaptive control over prosody. The model now intelligently adjusts speaking rate, pauses, and intonation based on textual context, delivering expressive, natural-sounding voice output that closely mimics real human speech.

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Performance

On objective benchmarks, Qwen3-Omni-Flash-2025-12-01 achieves substantial improvements across all modalities compared to Qwen3-Omni-Flash:

🧠 Stronger Text Understanding & Generation:

Major gains in logical reasoning (ZebraLogic +5.6), code generation (LiveCodeBench-v6 +9.3, MultiPL-E +2.7), and holistic writing quality (WritingBench +2.2), enabling more reliable execution of complex, multi-step instructions.

👂 More Accurate Speech Understanding:

Significantly lower word error rate on Fleurs-zh, along with a +3.2 improvement on VoiceBench, reflecting enhanced comprehension of spoken language in real-world dialogue scenarios.

🎙️More Natural Speech Synthesis:

Higher-quality, human-like voice generation across multiple languages—especially in Chinese and multilingual contexts—with improved prosody, pacing, and pausing that closely mirrors natural human speech.

👁️Deeper Image Understanding:

Breakthrough performance on visual reasoning tasks, including +4.7 on MMMU, +4.8 on MMMU-Pro, and +2.2 on MathVision_full, demonstrating a stronger ability to “see,” interpret, and reason about complex visual content—from diagrams to mathematical figures.

🎬 More Coherent Video Understanding:

Steady improvement in video semantic comprehension (MLVU +1.6), further strengthened by tighter audio-visual synchronization, laying a solid foundation for seamless real-time video conversations.

With this upgrade, Qwen3-Omni-Flash-2025-12-01 truly embodies the vision of “Hear You. See You. Follow Smarter.”—delivering an AI interaction experience that is more natural, precise, and vivid than ever before.

What’s Next

We are eager to hear your feedback and see the innovative applications you create with Qwen3-Omni. In the near future, we will further advance the model along multiple axes, including multi-speaker ASR, video OCR, audio–video proactive learning, and enhance support for agent-based workflows and function calling.

Citation

If you find our model helpful in your research, we’d appreciate a citation!

BibTeX
@misc{qwen3_omni_20251201,
author = {{Qwen Team, Alibaba}},
title = {{Qwen3-Omni-Flash-2025-12-01：Hear You. See You. Follow Smarter!}},
year = {2025},
url = {https://qwen.ai/blog?id=qwen3-omni-20251201},
urldate = {2025-12-09}
}

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Qwen3-Omni-Flash-2025-12-01: Hear You. See You. Follow Smarter!

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